The present disclosure relates to systems and methods for assessing body composition using computed tomography (CT) imaging. More particularly, the disclosure relates to systems and methods for assessing and quantifying abdominal muscle and fat to be used as biomarkers by automatically segmenting a CT scan of the abdomen.
There is a growing interest in the use of body composition (i.e., the amount of muscle and fat) as a biomarker, such as predicting outcome of cancer patients. For example, a wasting syndrome of advanced disease associates with shortened survival. Moreover, certain tissue compartments represent sites for drug distribution and are likely determinants of chemotherapy efficacy and toxicity. CT is considered a gold standard method used to assess body composition because of its high degree of specificity for the separate discrimination of many organs and tissues. However, the use of CT for assessing body composition in non-cancer populations is limited. CT scans of the abdomen are routinely obtained in the staging of cancer patients, and muscle and fat are readily distinguishable from other structures and could be quantified. Although these patients are routinely evaluated by high-resolution diagnostic imaging, the information content of these images is barely exploited, in part owing to lack of deployment of relevant methods and concepts in a cancer care setting.
Conventional manual segmentation of CT images uses defined windows of Hounsfield units (HU, units of radiation attenuation) for each tissue, and is guided by operator knowledge of anatomical structures. Automatic fat segmentation methods have been reported which are relatively straightforward owing to the unique HU ranges of adipose tissues. However, automated quantification of muscle, despite being highly related to human function and disease outcome, is more difficult. This latter task is particularly challenging owing to the large variability in muscle shape and the overlap in HU between muscle on the CT and abdominal organs, such as bowel, kidneys, liver and spleen. While manual segmentation of the muscle area is an option, it is time consuming and not practical for large scale clinical practice or research.
Thus, it would be beneficial to have systems and methods to automatically segment and quantify abdominal muscle and fat from CT images in a time efficient manner.